scholarly journals Causal Associations Between Educational Attainment and 14 Urological and Reproductive Health Outcomes: A Mendelian Randomization Study

2021 ◽  
Vol 9 ◽  
Author(s):  
Menghua Wang ◽  
Zhongyu Jian ◽  
Xiaoshuai Gao ◽  
Chi Yuan ◽  
Xi Jin ◽  
...  

Background: The impact of educational attainment (EA) on multiple urological and reproductive health outcomes has been explored in observational studies. Here we used Mendelian randomization (MR) to investigate whether EA has causal effects on 14 urological and reproductive health outcomes.Methods: We obtained summary statistics for EA and 14 urological and reproductive health outcomes from genome-wide association studies (GWAS). MR analyses were applied to explore the potential causal association between EA and them. Inverse variance weighted was the primary analytical method.Results: Genetically predicted one standard deviation (SD) increase in EA was causally associated with a higher risk of prostate cancer [odds ratio (OR) 1.14, 95% confidence interval (CI) 1.05–1.25, P = 0.003] and a reduced risk of kidney stone (OR 0.73, 95% CI 0.62–0.87, P < 0.001) and cystitis (OR 0.76, 95% CI 0.67–0.86, P < 0.001) after Bonferroni correction. EA was also suggestively correlated with a lower risk of prostatitis (OR 0.76, 95% CI 0.59–0.98, P = 0.037) and incontinence (OR 0.64, 95% CI 0.47–0.87, P = 0.004). For the bioavailable testosterone levels and infertility, sex-specific associations were observed, with genetically determined increased EA being related to higher levels of testosterone in men (β 0.07, 95% CI 0.04–0.10, P < 0.001), lower levels of testosterone in women (β −0.13, 95% CI−0.16 to−0.11, P < 0.001), and a lower risk of infertility in women (OR 0.74, 95% CI 0.64–0.86, P < 0.001) but was not related to male infertility (OR 0.79, 95% CI 0.52–1.20, P = 0.269) after Bonferroni correction. For bladder cancer, kidney cancer, testicular cancer, benign prostatic hyperplasia, and erectile dysfunction, no causal effects were observed.Conclusions: EA plays a vital role in urological diseases, especially in non-oncological outcomes and reproductive health. These findings should be verified in further studies when GWAS data are sufficient.

Author(s):  
Guanghao Qi ◽  
Nilanjan Chatterjee

Abstract Background Previous studies have often evaluated methods for Mendelian randomization (MR) analysis based on simulations that do not adequately reflect the data-generating mechanisms in genome-wide association studies (GWAS) and there are often discrepancies in the performance of MR methods in simulations and real data sets. Methods We use a simulation framework that generates data on full GWAS for two traits under a realistic model for effect-size distribution coherent with the heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank and performed down sampling to investigate trends in estimates of causal effects of these biomarkers on the risk of type 2 diabetes (T2D). Results Simulation studies show that weighted mode and MRMix are the only two methods that maintain the correct type I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS whereas the opposite is true for smaller sample sizes. Among the other methods, random-effect IVW (inverse-variance weighted method), MR-Robust and MR-RAPS (robust adjust profile score) tend to perform best in maintaining a low mean-squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real-data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on the risk of T2D across the different methods and estimates from many methods trended in one direction with increasing sample size with patterns similar to those observed in simulation studies. Conclusion The relative performance of different MR methods depends heavily on the sample sizes of the underlying GWAS, the proportion of valid instruments and the validity of the InSIDE assumption. Down-sampling analysis can be used in large GWAS for the possible detection of bias in the MR methods.


2017 ◽  
Author(s):  
Jorien L. Treur ◽  
Mark Gibson ◽  
Amy E Taylor ◽  
Peter J Rogers ◽  
Marcus R Munafò

AbstractStudy Objectives:Higher caffeine consumption has been linked to poorer sleep and insomnia complaints. We investigated whether these observational associations are the result of genetic risk factors influencing both caffeine consumption and poorer sleep, and/or whether they reflect (possibly bidirectional) causal effects.Methods:Summary-level data were available from genome-wide association studies (GWAS) on caffeine consumption (n=91,462), sleep duration, and chronotype (i.e., being a ‘morning’ versus an ‘evening’ person) (both n=128,266), and insomnia complaints (n=113,006). Linkage disequilibrium (LD) score regression was used to calculate genetic correlations, reflecting the extent to which genetic variants influencing caffeine consumption and sleep behaviours overlap. Causal effects were tested with bidirectional, two-sample Mendelian randomization (MR), an instrumental variable approach that utilizes genetic variants robustly associated with an exposure variable as an instrument to test causal effects. Estimates from individual genetic variants were combined using inverse-variance weighted meta-analysis, weighted median regression and MR Egger regression methods.Results:There was no clear evidence for genetic correlation between caffeine consumption and sleep duration (rg=0.000,p=0.998), chronotype (rg=0.086,p=0.192) or insomnia (rg=-0.034,p=0.700). Two-sample Mendelian randomization analyses did not support causal effects from caffeine consumption to sleep behaviours, or the other way around.Conclusions:We found no evidence in support of genetic correlation or causal effects between caffeine consumption and sleep. While caffeine may have acute effects on sleep when taken shortly before habitual bedtime, our findings suggest that a more sustained pattern of high caffeine consumption is likely associated with poorer sleep through shared environmental factors.


Author(s):  
Daniel B. Rosoff ◽  
Toni-Kim Clarke ◽  
Mark J. Adams ◽  
Andrew M. McIntosh ◽  
George Davey Smith ◽  
...  

Abstract Observational studies suggest that lower educational attainment (EA) may be associated with risky alcohol use behaviors; however, these findings may be biased by confounding and reverse causality. We performed two-sample Mendelian randomization (MR) using summary statistics from recent genome-wide association studies (GWAS) with >780,000 participants to assess the causal effects of EA on alcohol use behaviors and alcohol dependence (AD). Fifty-three independent genome-wide significant SNPs previously associated with EA were tested for association with alcohol use behaviors. We show that while genetic instruments associated with increased EA are not associated with total amount of weekly drinks, they are associated with reduced frequency of binge drinking ≥6 drinks (ßIVW = −0.198, 95% CI, −0.297 to –0.099, PIVW = 9.14 × 10−5), reduced total drinks consumed per drinking day (ßIVW = −0.207, 95% CI, −0.293 to –0.120, PIVW = 2.87 × 10−6), as well as lower weekly distilled spirits intake (ßIVW = −0.148, 95% CI, −0.188 to –0.107, PIVW = 6.24 × 10−13). Conversely, genetic instruments for increased EA were associated with increased alcohol intake frequency (ßIVW = 0.331, 95% CI, 0.267–0.396, PIVW = 4.62 × 10−24), and increased weekly white wine (ßIVW = 0.199, 95% CI, 0.159–0.238, PIVW = 7.96 × 10−23) and red wine intake (ßIVW = 0.204, 95% CI, 0.161–0.248, PIVW = 6.67 × 10−20). Genetic instruments associated with increased EA reduced AD risk: an additional 3.61 years schooling reduced the risk by ~50% (ORIVW = 0.508, 95% CI, 0.315–0.819, PIVW = 5.52 × 10−3). Consistency of results across complementary MR methods accommodating different assumptions about genetic pleiotropy strengthened causal inference. Our findings suggest EA may have important effects on alcohol consumption patterns and may provide potential mechanisms explaining reported associations between EA and adverse health outcomes.


Author(s):  
Xichang Wang ◽  
Xiaotong Gao ◽  
Yutong Han ◽  
Fan Zhang ◽  
Zheyu Lin ◽  
...  

Abstract Context The association between serum thyroid-stimulating hormone (TSH) and obesity traits has been investigated previously in several epidemiological studies. However, the underlying causal association has not been established. Objective To determine and analyze the causal association between serum TSH level and obesity-related traits (BMI and obesity). Design, Setting, Participants The latest genome-wide association studies (GWASs) on TSH, BMI and obesity were searched to obtain full statistics. Bidirectional two-sample Mendelian randomization (MR) was performed to explore the causal relationship between serum TSH and BMI and obesity. The inverse variance-weighted (IVW) and MR-Egger methods were used to combine the estimation for each SNP. Based on the preliminary MR results, free thyroxine (fT4) and free triiodothyronine (fT3) levels were also set as outcomes to further analyze the impact of BMI on them. Main Outcome Measures BMI and obesity were treated as the outcomes to evaluate the effect of serum TSH on them, and TSH was set as the outcome to estimate the effect of BMI and obesity on it. Results Both IVW and MR-Egger results indicated that genetically driven serum TSH did not causally lead to changes in BMI or obesity. Moreover, the IVW method showed that the TSH level could be significantly elevated by genetically predicted high BMI (β=0.038, se=0.013, p=0.004). In further MR analysis, the IVW method indicated that BMI could causally increase the fT3 (β=10.123, se=2.523, p<0.001) while not significantly affecting the fT4 level. Conclusion Together with fT3, TSH can be significantly elevated by an increase in genetically driven BMI.


Author(s):  
Shunsuke Katsuhara ◽  
Maki Yokomoto-Umakoshi ◽  
Hironobu Umakoshi ◽  
Yayoi Matsuda ◽  
Norifusa Iwahashi ◽  
...  

Abstract Purpose Prolonged exposure to pathological cortisol, as in Cushing’s syndrome causes various age-related disorders including sarcopenia. However, it is unclear whether mild cortisol excess, for example, accelerates sarcopenia due to aging or chronic stress. We performed a Mendelian randomization (MR) analysis to assess whether cortisol was causally associated with muscle strength and mass. Methods Three single nucleotide polymorphisms associated with plasma cortisol concentrations in the CORtisol NETwork consortium (n = 12,597) were used as instrumental variables. Summary statistics with traits of interest were obtained from relevant genome-wide association studies. For the primary analysis, we used the fixed-effects inverse-variance weighted analysis accounting for genetic correlations between variants. Results One standard deviation (SD) increase in cortisol was associated with SD reduction in grip strength (estimate, -0.032; 95% confidence interval [CI] -0.044 ~ -0.020; P = 3e-04), whole-body lean mass (estimate, -0.032; 95%CI, -0.046 ~ -0.017; P = 0.004), and appendicular lean mass (estimate, -0.031; 95%CI, -0.049 ~ -0.012; P = 0.001). The results were supported by the weighted-median analysis, with no evidence of pleiotropy in the MR-Egger analysis. The association of cortisol with grip strength and lean mass was observed in women but not in men. The association was attenuated after adjusting for fasting glucose in the multivariable MR analysis, which was the top mediator for the association in the MR-Bayesian model averaging analysis. Conclusion This MR study provides evidence for the association of cortisol with reduced muscle strength and mass, suggesting the impact of cortisol on the development of sarcopenia.


2020 ◽  
Author(s):  
Lanlan Chen ◽  
Aowen Tian ◽  
Zhipeng Liu ◽  
Miaoran Zhang ◽  
Xingchen Pan ◽  
...  

ABSTRACTBackgroundIt remains controversial whether daytime napping is beneficial for human health.ObjectiveTo examine the causal relationship between daytime napping and the risk for various human diseases.DesignPhenotype-wide Mendelian randomization study.SettingNon-UK Biobank cohorts reported in published genome-wide association studies (GWAS) provided the outcome phenotypes in the discovery stage. The UK Biobank cohort provided the outcome phenotypes in the validation stage.ParticipantsThe UK Biobank GWAS included 361,194 European-ancestry residents in the UK. Non-UKBB GWAS included various numbers of participants.ExposureSelf-reported daytime napping frequency.Main outcome measureA wide-spectrum of human health outcomes including obesity, major depressive disorder, and high cholesterol.MethodsWe examined the causal relationship between daytime napping frequency in the UK Biobank as exposure and a panel of 1,146 health outcomes reported in genome-wide association studies (GWAS), using a two-sample Mendelian randomization analysis. The significant findings were further validated in the UK Biobank health outcomes of 4,203 human traits and diseases. The causal effects were estimated using a fixed-effect inverse variance weighted model. MR-Egger intercept test was applied to detect horizontal pleiotropy, along with Cochran’s Q test to assess heterogeneity among the causal effects of IVs.FindingsThere were significant causal relationships between daytime napping frequency and a wide spectrum of human health outcomes. In particular, we validated that frequent daytime napping increased the risks of major depressive disorder, obesity and abnormal lipid profile.InterpretationThe current study showed that frequent daytime napping mainly had adverse impacts on physical and mental health. Cautions should be taken for health recommendations on daytime napping. Further studies are necessary to precisely define the best daytime napping strategies.


Author(s):  
Leon G. Martens ◽  
Jiao Luo ◽  
Ko Willems van Dijk ◽  
J. Wouter Jukema ◽  
Raymond Noordam ◽  
...  

Background Dietary intake and blood concentrations of vitamins E and C, lycopene, and carotenoids have been associated with a lower risk of incident (ischemic) stroke. However, causality cannot be inferred from these associations. Here, we investigated causality by analyzing the associations between genetically influenced antioxidant levels in blood and ischemic stroke using Mendelian randomization. Methods and Results For each circulating antioxidant (vitamins E and C, lycopene, β‐carotene, and retinol), which were assessed as either absolute blood levels and/or high‐throughput metabolite levels, independent genetic instrumental variables were selected from earlier genome‐wide association studies ( P <5×10 −8 ). We used summary statistics for single‐nucleotide polymorphisms–stroke associations from 3 European‐ancestry cohorts (cases/controls): MEGASTROKE (60 341/454 450), UK Biobank (2404/368 771), and the FinnGen study (8046/164 286). Mendelian randomization analyses were performed on each exposure per outcome cohort using inverse variance–weighted analyses and subsequently meta‐analyzed. In a combined sample of 1 058 298 individuals (70 791 cases), none of the genetically influenced absolute antioxidants or antioxidant metabolite concentrations were causally associated with a lower risk of ischemic stroke. For absolute antioxidants levels, the odds ratios (ORs) ranged between 0.94 (95% CI, 0.85–1.05) for vitamin C and 1.04 (95% CI, 0.99–1.08) for lycopene. For metabolites, ORs ranged between 1.01 (95% CI, 0.98–1.03) for retinol and 1.12 (95% CI, 0.88–1.42) for vitamin E. Conclusions This study did not provide evidence for a causal association between dietary‐derived antioxidant levels and ischemic stroke. Therefore, antioxidant supplements to increase circulating levels are unlikely to be of clinical benefit to prevent ischemic stroke.


2021 ◽  
Vol 11 (12) ◽  
pp. 1306
Author(s):  
Alice Giontella ◽  
Luca A. Lotta ◽  
John D. Overton ◽  
Aris Baras ◽  
Andrea Sartorio ◽  
...  

Thyroid function has a widespread effect on the cardiometabolic system. However, the causal association between either subclinical hyper- or hypothyroidism and the thyroid hormones with blood pressure (BP) and cardiovascular diseases (CVD) is not clear. We aim to investigate this in a two-sample Mendelian randomization (MR) study. Single nucleotide polymorphisms (SNPs) associated with thyroid-stimulating hormone (TSH), free tetraiodothyronine (FT4), hyper- and hypothyroidism, and anti-thyroid peroxidase antibodies (TPOAb), from genome-wide association studies (GWAS), were selected as MR instrumental variables. SNPs–outcome (BP, CVD) associations were evaluated in a large-scale cohort, the Malmö Diet and Cancer Study (n = 29,298). Causal estimates were computed by inverse-variance weighted (IVW), weighted median, and MR-Egger approaches. Genetically increased levels of TSH were associated with decreased systolic BP and with a lower risk of atrial fibrillation. Hyperthyroidism and TPOAb were associated with a lower risk of atrial fibrillation. Our data support a causal association between genetically decreased levels of TSH and both atrial fibrillation and systolic BP. The lack of significance after Bonferroni correction and the sensitivity analyses suggesting pleiotropy, should prompt us to be cautious in their interpretation. Nevertheless, these findings offer mechanistic insight into the etiology of CVD. Further work into the genes involved in thyroid functions and their relation to cardiovascular outcomes may highlight pathways for targeted intervention.


Rheumatology ◽  
2021 ◽  
Author(s):  
Sizheng Steven Zhao ◽  
Michael V Holmes ◽  
Jie Zheng ◽  
Eleanor Sanderson ◽  
Alice R Carter

Abstract Objective To estimate the causal relationship between educational attainment—as a proxy for socioeconomic inequality—and risk of RA, and quantify the roles of smoking and BMI as potential mediators. Methods Using the largest genome-wide association studies (GWAS), we performed a two-sample Mendelian randomization (MR) study of genetically predicted educational attainment (instrumented using 1265 variants from 766 345 individuals) and RA (14 361 cases, 43 923 controls). We used two-step MR to quantify the proportion of education’s effect on RA mediated by smoking exposure (as a composite index capturing duration, heaviness and cessation, using 124 variants from 462 690 individuals) and BMI (517 variants, 681 275 individuals), and multivariable MR to estimate proportion mediated by both factors combined. Results Each S.d. increase in educational attainment (4.2 years of schooling) was protective of RA (odds ratio 0.37; 95% CI: 0.31, 0.44). Higher educational attainment was also protective for smoking exposure (β = −0.25 S.d.; 95% CI: −0.26, −0.23) and BMI [β = −0.27 S.d. (∼1.3 kg/m2); 95% CI: −0.31, −0.24]. Smoking mediated 24% (95% CI: 13%, 35%) and BMI 17% (95% CI: 11%, 23%) of the total effect of education on RA. Combined, the two risk factors explained 47% (95% CI: 11%, 82%) of the total effect. Conclusion Higher educational attainment has a protective effect on RA risk. Interventions to reduce smoking and excess adiposity at a population level may reduce this risk, but a large proportion of education’s effect on RA remains unexplained. Further research into other risk factors that act as potentially modifiable mediators are required.


Author(s):  
Qing Cheng ◽  
Tingting Qiu ◽  
Xiaoran Chai ◽  
Baoluo Sun ◽  
Yingcun Xia ◽  
...  

Abstract Motivation Mendelian randomization (MR) is a valuable tool to examine the causal relationships between health risk factors and outcomes from observational studies. Along with the proliferation of genome-wide association studies, a variety of two-sample MR methods for summary data have been developed to account for horizontal pleiotropy (HP), primarily based on the assumption that the effects of variants on exposure (γ) and HP (α) are independent. In practice, this assumption is too strict and can be easily violated because of the correlated HP. Results To account for this correlated HP, we propose a Bayesian approach, MR-Corr2, that uses the orthogonal projection to reparameterize the bivariate normal distribution for γ and α, and a spike-slab prior to mitigate the impact of correlated HP. We have also developed an efficient algorithm with paralleled Gibbs sampling. To demonstrate the advantages of MR-Corr2 over existing methods, we conducted comprehensive simulation studies to compare for both type-I error control and point estimates in various scenarios. By applying MR-Corr2 to study the relationships between exposure–outcome pairs in complex traits, we did not identify the contradictory causal relationship between HDL-c and CAD. Moreover, the results provide a new perspective of the causal network among complex traits. Availability and implementation The developed R package and code to reproduce all the results are available at https://github.com/QingCheng0218/MR.Corr2. Supplementary information Supplementary data are available at Bioinformatics online.


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